The use of the term ``landscapes'' is increasing rapidly in the field of evolutionary computation, yet in many cases it remains poorly, if at all, defined. This situtation has perhaps developed because everyone grasps the imagery immediately, and the questions that would be asked of a less evocative term do not get asked. This paper presents a model of landscapes that is general enough to encompass most of what computer scientists would call search, though the model is not restricted to either the field or the viewpoint. It is particularly relevant to algorithms that employ some form of crossover, and hence to genetic algorithms and other members of the evolutionary computing family. An overview of the consequences and properties of the model establishes a connection with more traditional search algorithms from artificial intelligence, introduces the notion of a crossover landscape, and argues the importance of viewing search as navigation and structure.
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Paper provided by Santa Fe Institute in its series Working Papers with number
94-02-002.
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